{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder for Anomaly Detection with scikit-learn, Keras and TensorFlow\n",
    "\n",
    "This script trains an autoencoder for anomaly detection. We use Python, scikit-learn, TensorFlow and Keras to prepare the data and train the model.\n",
    "\n",
    "The input data is sensor data. Here is one example:\n",
    "\n",
    "2.10# 2.13# 2.19# 2.28# 2.44# 2.62# 2.80# 3.04# 3.36# 3.69# 3.97# 4.24# 4.53#4.80# 5.02# 5.21# 5.40# 5.57# 5.71# 5.79# 5.86# 5.92# 5.98# 6.02# 6.06# 6.08# 6.14# 6.18# 6.22# 6.27#6.32# 6.35# 6.38# 6.45# 6.49# 6.53# 6.57# 6.64# 6.70# 6.73# 6.78# 6.83# 6.88# 6.92# 6.94# 6.98# 7.01#7.03# 7.05# 7.06# 7.07# 7.08# 7.06# 7.04# 7.03# 6.99# 6.94# 6.88# 6.83# 6.77# 6.69# 6.60# 6.53# 6.45#6.36# 6.27# 6.19# 6.11# 6.03# 5.94# 5.88# 5.81# 5.75# 5.68# 5.62# 5.61# 5.54# 5.49# 5.45# 5.42# 5.38#5.34# 5.31# 5.30# 5.29# 5.26# 5.23# 5.23# 5.22# 5.20# 5.19# 5.18# 5.19# 5.17# 5.15# 5.14# 5.17# 5.16#5.15# 5.15# 5.15# 5.14# 5.14# 5.14# 5.15# 5.14# 5.14# 5.13# 5.15# 5.15# 5.15# 5.14# 5.16# 5.15# 5.15#5.14# 5.14# 5.15# 5.15# 5.14# 5.13# 5.14# 5.14# 5.11# 5.12# 5.12# 5.12# 5.09# 5.09# 5.09# 5.10# 5.08# 5.08# 5.08# 5.08# 5.06# 5.05# 5.06# 5.07# 5.05# 5.03# 5.03# 5.04# 5.03# 5.01# 5.01# 5.02# 5.01# 5.01#5.00# 5.00# 5.02# 5.01# 4.98# 5.00# 5.00# 5.00# 4.99# 5.00# 5.01# 5.02# 5.01# 5.03# 5.03# 5.02# 5.02#5.04# 5.04# 5.04# 5.02# 5.02# 5.01# 4.99# 4.98# 4.96# 4.96# 4.96# 4.94# 4.93# 4.93# 4.93# 4.93# 4.93# 5.02# 5.27# 5.80# 5.94# 5.58# 5.39# 5.32# 5.25# 5.21# 5.13# 4.97# 4.71# 4.39# 4.05# 3.69# 3.32# 3.05#2.99# 2.74# 2.61# 2.47# 2.35# 2.26# 2.20# 2.15# 2.10# 2.08\n",
    "\n",
    "The autoencoder compresses the data and tries to reconstruct it. The reconstruction error finds anomalies.\n",
    "\n",
    "This Jupyter notebook was tested with Python 3.6.7, Numpy 1.16.4, scikit-learn 0.20.1 and tensorflow 1.13.1. \n",
    "\n",
    "Kudos to David Ellison who created the foundation for this example: https://www.datascience.com/blog/fraud-detection-with-tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import packages\n",
    "# matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve\n",
    "from sklearn.metrics import recall_score, classification_report, auc, roc_curve\n",
    "from sklearn.metrics import precision_recall_fscore_support, f1_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pylab import rcParams\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.1\n"
     ]
    }
   ],
   "source": [
    "#check TensorFlow verson: 1.x or 2.0?\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set random seed and percentage of test data\n",
    "RANDOM_SEED = 314 #used to help randomly select the data points\n",
    "TEST_PCT = 0.2 # 20% of the data\n",
    "\n",
    "#set up graphic style in this case I am using the color scheme from xkcd.com\n",
    "rcParams['figure.figsize'] = 14, 8.7 # Golden Mean\n",
    "LABELS = [\"Normal\",\"Fraud\"]\n",
    "#col_list = [\"cerulean\",\"scarlet\"]# https://xkcd.com/color/rgb/\n",
    "#sns.set(style='white', font_scale=1.75, palette=sns.xkcd_palette(col_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data/sensor_data.csv\") #unzip and read in data downloaded to the local directory\n",
    "df.head(n=5) #just to check you imported the dataset properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #secondary check on the size of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any() #check to see if any values are null, which there are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Class'], sort = True) #class comparison 0=Normal 1=Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#if you don't have an intuitive sense of how imbalanced these two classes are, let's go visual\n",
    "count_classes = pd.value_counts(df['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.title(\"Frequency by observation number\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Number of Observations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_df = df[df.Class == 0] #save normal_df observations into a separate df\n",
    "fraud_df = df[df.Class == 1] #do the same for frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot of high value transactions\n",
    "bins = np.linspace(200, 2500, 100)\n",
    "plt.hist(normal_df.Amount, bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist(fraud_df.Amount, bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Amount by percentage of transactions (transactions \\$200+)\")\n",
    "plt.xlabel(\"Transaction amount (USD)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 48, 48) #48 hours\n",
    "plt.hist((normal_df.Time/(60*60)), bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist((fraud_df.Time/(60*60)), bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Percentage of transactions by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "#plt.hist((df.Time/(60*60)),bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter((normal_df.Time/(60*60)), normal_df.Amount, alpha=0.6, label='Normal')\n",
    "plt.scatter((fraud_df.Time/(60*60)), fraud_df.Amount, alpha=0.9, label='Fraud')\n",
    "plt.title(\"Amount of transaction by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel('Amount (USD)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = df.drop(['Time'], axis=1) #if you think the var is unimportant\n",
    "df_norm = df\n",
    "df_norm['Time'] = StandardScaler().fit_transform(df_norm['Time'].values.reshape(-1, 1))\n",
    "df_norm['Amount'] = StandardScaler().fit_transform(df_norm['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x = train_test_split(df_norm, test_size=TEST_PCT, random_state=RANDOM_SEED)\n",
    "train_x = train_x[train_x.Class == 0] #where normal transactions\n",
    "train_x = train_x.drop(['Class'], axis=1) #drop the class column (as Autoencoder is unsupervised and does not need / use labels for training)\n",
    "\n",
    "# test_x (without class) for validation; test_y (with Class) for prediction + calculating MSE / reconstruction error\n",
    "test_y = test_x['Class'] #save the class column for the test set\n",
    "test_x = test_x.drop(['Class'], axis=1) #drop the class column\n",
    "\n",
    "train_x = train_x.values #transform to ndarray\n",
    "test_x = test_x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227468, 30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# Reduce number of epochs and batch_size if your Jupyter crashes (due to memory issues) \n",
    "# or if you just want to demo and not run it for so long.\n",
    "# nb_epoch = 100\n",
    "# batch_size = 128\n",
    "nb_epoch = 5\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "# Autoencoder: 30 => 14 => 7 => 7 => 14 => 30 dimensions\n",
    "input_dim = train_x.shape[1] #num of columns, 30\n",
    "encoding_dim = 14\n",
    "hidden_dim = int(encoding_dim / 2) #i.e. 7\n",
    "learning_rate = 1e-7\n",
    "\n",
    "# Dense = fully connected layer \n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "# First parameter is output units (14 then 7 then 7 then 30) : \n",
    "encoder = Dense(encoding_dim, activation=\"tanh\", activity_regularizer=regularizers.l1(learning_rate))(input_layer)\n",
    "encoder = Dense(hidden_dim, activation=\"relu\")(encoder)\n",
    "decoder = Dense(hidden_dim, activation='tanh')(encoder)\n",
    "decoder = Dense(input_dim, activation='relu')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 227468 samples, validate on 56962 samples\n",
      "Epoch 1/5\n",
      "227468/227468 [==============================] - 17s 74us/step - loss: 0.7334 - acc: 0.6589 - val_loss: 0.7604 - val_acc: 0.6673\n",
      "Epoch 2/5\n",
      "227468/227468 [==============================] - 13s 58us/step - loss: 0.7216 - acc: 0.6770 - val_loss: 0.7528 - val_acc: 0.6840\n",
      "Epoch 3/5\n",
      "227468/227468 [==============================] - 12s 52us/step - loss: 0.7160 - acc: 0.6839 - val_loss: 0.7490 - val_acc: 0.6877\n",
      "Epoch 4/5\n",
      "227468/227468 [==============================] - 11s 49us/step - loss: 0.7126 - acc: 0.6880 - val_loss: 0.7465 - val_acc: 0.6934\n",
      "Epoch 5/5\n",
      "227468/227468 [==============================] - 12s 55us/step - loss: 0.7103 - acc: 0.6926 - val_loss: 0.7469 - val_acc: 0.6875\n"
     ]
    }
   ],
   "source": [
    "autoencoder.compile(metrics=['accuracy'],\n",
    "                    loss='mean_squared_error',\n",
    "                    optimizer='adam')\n",
    "\n",
    "cp = ModelCheckpoint(filepath=\"../../models/autoencoder_sensor_anomaly_detection.h5\",\n",
    "                               save_best_only=True,\n",
    "                               verbose=0)\n",
    "\n",
    "tb = TensorBoard(log_dir='./logs',\n",
    "                histogram_freq=0,\n",
    "                write_graph=True,\n",
    "                write_images=True)\n",
    "\n",
    "history = autoencoder.fit(train_x, train_x, # Autoencoder => Input == Output dimensions!\n",
    "                    epochs=nb_epoch,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_data=(test_x, test_x), # Autoencoder => Input == Output dimensions!\n",
    "                    verbose=1,\n",
    "                    callbacks=[cp, tb]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "autoencoder = load_model('../../models/autoencoder_sensor_anomaly_detection.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'], linewidth=2, label='Train')\n",
    "plt.plot(history['val_loss'], linewidth=2, label='Test')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "#plt.ylim(ymin=0.70,ymax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reconstruction_error</th>\n",
       "      <th>True_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56962.000000</td>\n",
       "      <td>56962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.746469</td>\n",
       "      <td>0.002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.281421</td>\n",
       "      <td>0.044887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.051002</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.258271</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.404302</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.641217</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>197.051534</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Reconstruction_error    True_class\n",
       "count          56962.000000  56962.000000\n",
       "mean               0.746469      0.002019\n",
       "std                3.281421      0.044887\n",
       "min                0.051002      0.000000\n",
       "25%                0.258271      0.000000\n",
       "50%                0.404302      0.000000\n",
       "75%                0.641217      0.000000\n",
       "max              197.051534      1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_predictions = autoencoder.predict(test_x)\n",
    "mse = np.mean(np.power(test_x - test_x_predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'Reconstruction_error': mse,\n",
    "                        'True_class': test_y})\n",
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_pos_rate, true_pos_rate, thresholds = roc_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "roc_auc = auc(false_pos_rate, true_pos_rate,)\n",
    "\n",
    "plt.plot(false_pos_rate, true_pos_rate, linewidth=5, label='AUC = %0.3f'% roc_auc)\n",
    "plt.plot([0,1],[0,1], linewidth=5)\n",
    "\n",
    "plt.xlim([-0.01, 1])\n",
    "plt.ylim([0, 1.01])\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Receiver operating characteristic curve (ROC)')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision_rt, recall_rt, threshold_rt = precision_recall_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "plt.plot(recall_rt, precision_rt, linewidth=5, label='Precision-Recall curve')\n",
    "plt.title('Recall vs Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(threshold_rt, precision_rt[1:], label=\"Precision\",linewidth=5)\n",
    "plt.plot(threshold_rt, recall_rt[1:], label=\"Recall\",linewidth=5)\n",
    "plt.title('Precision and recall for different threshold values')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision/Recall')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_fixed = 5\n",
    "groups = error_df.groupby('True_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.Reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Fraud\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold_fixed, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = [1 if e > threshold_fixed else 0 for e in error_df.Reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.True_class, pred_y)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
